Transferring color to greyscale images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Colorization using optimization
ACM SIGGRAPH 2004 Papers
Color2Gray: salience-preserving color removal
ACM SIGGRAPH 2005 Papers
Color Traits Transfer to Grayscale Images
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
Image-based facial sketch-to-photo synthesis via online coupled dictionary learning
Information Sciences: an International Journal
Fast image and video colorization using chrominance blending
IEEE Transactions on Image Processing
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Colorization for gray scale facial image is an important technique in various practical applications. However, the methods that have been proposed are essentially semi-automatic. In this paper, we present a new probabilistic framework based on Maximum A Posteriori (MAP) estimation to automatically transform the given gray scale facial image to corresponding color one. Firstly, the input image is divided into several patches and non-parametric Markov random field (MRF) is employed to formulate the global energy. Secondly, Locality-constrained Linear Coding (LLC) is employed to learn the color distribution for each patch. At the same time, the simulated annealing algorithm is employed to iteratively update the patches chosen by LLC to optimize the MRF by decreasing global energy cost. The experimental results demonstrate that the proposed framework is effective to colorize the gray scale facial images to corresponding color ones.